Introduction

This is about A* algorithm implementation which is about the way how we can find a best path between two positions. I already know that there are other A* implementations in this codeproject site. They are good, but I bet this is more simple and an easy implementation for beginners to understand.

There is no unnecessary code in this implementation, I just implement the A* algorithm pseudocode step by step in very intuitive ways. As you can see in the picture above, '#' means wall, each dot means available road, and 'o' means path that AI finds.

About A* algorithm

Shorty, A* is the most popular pathfinding algorithm to go from start to goal, based on efficiency of movement cost.

You can see pseudocode for A* that I used in this implementation at Justin Heyes-Jones A* tutorial. Visit here (http://www.geocities.com/jheyesjones/pseudocode.html) to see orginal pseudocode. The print-out of this pseudocode will help you a lot to understand this implementation.

Class Design

There are three primary classes in this implementation.

Map - This class represents a map.

Node - This class represents each tile on the map. It has two primary methods.

CompareTo - This method allows us to decide which node is better. We can say current node is better if this method returns negative number, which means current node has lower cost than the other node being compared.

isMatch - This allows us to decide whether two node's geomatical positions are same or not.

SortedCostNodeList - This is a list that stores Node object list. We need to get off the lowest cost node from the list, so this list is implemented as sorted list order by cost value of the node, and we don't need to examine the costs of all elements in the list every time to pop the node which has the lowest cost because they are already sorted. Just pop one.This list has two primary methods.

push - add node elements to the list at proper position order by node cost.

Node's CompareTo method is used internally to sort order by cost.pop - just returns the lowest cost node from the list and remove it from the list.

Implementation

This is the core loop of the algorithm.

ArrayList SolutionPathList = new ArrayList();
//Create a node containing the goal state node_goal
Node node_goal = new Node(null,null,1,15,15);
//Create a node containing the start state node_start
Node node_start = new Node(null,node_goal,1,0,0);
//Create OPEN and CLOSED list
SortedCostNodeList OPEN = new SortedCostNodeList ();
SortedCostNodeList CLOSED = new SortedCostNodeList ();
//Put node_start on the OPEN list
OPEN.push (node_start);
//while the OPEN list is not empty
while (OPEN.Count>0)
{
//Get the node off the open list
//with the lowest f and call it node_current
Node node_current = OPEN.pop ();
//if node_current is the same state as node_goal we
//have found the solution;
//break from the while loop;
if (node_current.isMatch (node_goal))
{
node_goal.parentNode = node_current.parentNode ;
break;
}
//Generate each state node_successor that can come after node_current
ArrayList successors = node_current.GetSuccessors ();
//for each node_successor or node_current
foreach (Node node_successor in successors)
{
//Set the cost of node_successor to be the cost of node_current plus
//the cost to get to node_successor from node_current
//--> already set while we were getting successors
//find node_successor on the OPEN list
int oFound = OPEN.IndexOf (node_successor);
//if node_successor is on the OPEN list but the existing one is as good
//or better then discard this successor and continue
if (oFound>0)
{
Node existing_node = OPEN.NodeAt (oFound);
if (existing_node.CompareTo (node_current) <= 0)
continue;
}
//find node_successor on the CLOSED list
int cFound = CLOSED.IndexOf (node_successor);
//if node_successor is on the CLOSED list
//but the existing one is as good
//or better then discard this successor and continue;
if (cFound>0)
{
Node existing_node = CLOSED.NodeAt (cFound);
if (existing_node.CompareTo (node_current) <= 0 )
continue;
}
//Remove occurences of node_successor from OPEN and CLOSED
if (oFound!=-1)
OPEN.RemoveAt (oFound);
if (cFound!=-1)
CLOSED.RemoveAt (cFound);
//Set the parent of node_successor to node_current;
//--> already set while we were getting successors
//Set h to be the estimated distance to node_goal
//(Using heuristic function)
//--> already set while we were getting successors
//Add node_successor to the OPEN list
OPEN.push (node_successor);
}
//Add node_current to the CLOSED list
CLOSED.push (node_current);
}

Once we get to the goal, follow parent nodes to find the solution path.

This A* implementation is very simple and good for beginners who want to know how A* algorithm works. Change map data in variety of ways, and check out how AI is smart to find the good path. Enjoy your programming.

License

This article has no explicit license attached to it but may contain usage terms in the article text or the download files themselves. If in doubt please contact the author via the discussion board below.

Many thanks for your well-written article! It made it very easy for me to grasp the mechanics of the algorithm and provided me with a very good start on A*. Your article focuses on the essentials and your implementation reflects the conceptual structure of A* in a very crisp manner.

I have written an article in response to this implementation. Please take a look and let me know what you think. If you are unhappy with it, please let me know and I will either take it down or amend it.

Hi, I'm student at university, and I had to implement a A*. But all the projects I found were to big, and mixed with a lot of unnecessary code, like classes that implement some visual panels, mazes... And they didn't show the algorithm clearly.
This project was perfect for my needs. I just needed the algorithm A*. Now I can implemented in my way, and needs, because it was really easy to understand.
I will need to implement A* in NS2 for some mobile sensor network.Wish my luck.
seunghyop - Thanks again.

I'm trying to implement this code into a turn based RogueLike console app very similar to the map in this example. Could someone give me some help in how to attach this to a robot - stepping through the path via a keystroke - instead of displaying the complete path from the start???

Iv been trying to impliment my own version of the A* algorithm. I have came close but have failed(3 times). It seems so simple but I just cant get it right Im going to use your design and create a .dll for use in one of my games..

I have now seen this issue in two algorithms I have looked at... If someone is walking through a maze, why would they make an unnecessary diagonal step... I believe that a horizontal or vertical step should be worth 1, and a diagonal step should be worth 1.5 (as diagonal would we worth 1.414 more than a horizontal or vertical step).

If you were to give a cost to a diagonal movement of 1.5, there would be no unnecessary diagonal movements unless they were shorter than a vertical or horizontal movement...

Your issue with the algorithm is due to a false assumption about the world where the search takes place. A diagonal move may appear to be at a greater distance than a horizontal or vertical move, but the only thing we care about is the cost to make the move. It appears that in this search world the cost to make a diagonal move is the same as a horizontal or vertical move. The reason the result ends up with diagonal moves is the coder's choice to expand successor nodes in an order that happens to give priority to diagonal moves. Visually, it's a little confusing (and a little odd) but logically it works fine.

Truly, a much more interesting implementation of A* would have variable cost-to-go's all throughout the world. That way it wouldn't just be an issue of where are the walls or what's the shortest path but rather a question of which path has the least cost. And really, that's the main function of A*. Using A* in a world where all moves have the same cost is overkill.